Developing a biomechanical model-based elasticity imaging method for assessing hormone receptor positive breast cancer treatment-related myocardial stiffness changes

Miller, Collin; Jordan, Jennifer H.; Thomas, Alexandra; Weis, Jared A.

doi:10.1117/1.jmi.8.5.056002

Cited by 5 publications

(17 citation statements)

References 46 publications

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“…This intentional parsimonious modeling approach was developed to utilize routine clinically relevant images to facilitate eventual clinical implementation and has shown sensitivity to changes in myocardial stiffness in the previous work. 11 As an initial step toward quantifying the impact of neglecting viscoelasticity, we assessed heart rate variability by calculating differences in heart rate for both within-session and between-session image acquisitions to assess a potential source of methodology variation. Heart rate variability was found to be minimal for within-session images and moderate for between-session images, representing a potential source of variability to address in future work.…”

Section: Discussionmentioning

confidence: 99%

“…In the previous work, we developed and investigated a noninvasive BEIM for identifying changes in LV stiffness to indicate preclinical cardiotoxicity due to antineoplastic therapies. 11 This method employs a simple biomechanical model to estimate direct and functional spatial mechanical elasticity using clinically relevant cardiac MR imaging without the need for exogenous contrast agents or complex imaging sequences. In this study, we assessed the reproducibility of circumferential, radial, and shear modulus maps along with mechanical FMSA values in a publicly available imaging cohort of participants with no expectation of cardiovascular disease.…”

Section: Discussionmentioning

confidence: 99%

“…To compare elasticity maps across image acquisitions, in the previous work, we normalized estimated elasticity modulus maps by the median elasticity. 11 In this work, we improve this comparison by normalizing elasticity maps by the time-stress-integral 29 calculated over the endocardium based on the elasticity solution. This normalization factor is representative of the time resolved stress experienced by the endocardium throughout passive diastole and was calculated separately for each cardiac cine MR image acquisition.…”

Section: Beimmentioning

confidence: 99%

“…Estimated observed deformation was interpolated onto the finite-element mesh of the LV for iterative comparison to model-based deformation estimations for the inverse parameter estimation framework. 11…”

Section: Image Processingmentioning

confidence: 99%

“…In a previous proof-of-concept correlative imaging study utilizing routine cardiac MR images, we demonstrated LV elasticity differences between cohorts of premenopausal breast cancer patients either undergoing ovarian function suppression concurrent with aromatase inhibitor administration for hormone receptor positive breast cancer or after completion of therapy for triple negative breast cancer. 11 To further interrogate BEIM, in this study we perform a reproducibility analysis for LV mechanical elasticity measures using publicly available cardiac MR images acquired in a previous study for reproducibility assessment of myocardial radiomic features in a cohort with no known cardiovascular disease. 12 Test and retest images were acquired within an initial imaging session and participants returned for repeat test and retest image acquisitions during a second imaging session after two weeks.…”

Section: Introductionmentioning

confidence: 99%

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Reproducibility assessment of a biomechanical model-based elasticity imaging method for identifying changes in left ventricular mechanical stiffness

et al. 2022

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. Purpose Cardiotoxicity of antineoplastic therapies is increasingly a risk to cancer patients treated with curative intent with years of life to protect. Studies highlight the importance of identifying early cardiac decline in cancer patients undergoing cardiotoxic therapies. Accurate tools to study this are a critical clinical need. Current and emerging methods for assessing cardiotoxicity are too coarse for identifying preclinical cardiac degradation or too cumbersome for clinical implementation. Approach In the previous work, we developed a noninvasive biomechanical model-based elasticity imaging methodology (BEIM) to assess mechanical stiffness changes of the left ventricle (LV) based on routine cine cardiac magnetic resonance (CMR) images. We examine this methodology to assess methodological reproducibility. We assessed a cohort of 10 participants that underwent test/retest short-axis CMR imaging at baseline and follow-up sessions as part of a previous publicly available study. We compare test images to retest images acquired within the same session to assess within-session reproducibility. We also compare test and retest images acquired at the baseline imaging session to test and retest images acquired at the follow-up imaging session to assess between-session reproducibility. Results We establish the within-session and between-session reproducibility of our method, with global elasticity demonstrating repeatability within a range previously demonstrated in cardiac strain imaging studies. We demonstrate increased repeatability of global elasticity compared to segmental elasticity for both within-session and between-session. Within-subject coefficients of variation for within-session test/retest images globally for all modulus directions and a mechanical fractional mechanical stiffness anisotropy metric ranged from 11% to 28%. Conclusions Results suggest that our methodology can reproducibly generate estimates of relative mechanical elasticity of the LV and provides a threshold for distinguishing true changes in myocardial mechanical stiffness from experimental variation. BEIM has applications in identifying preclinical cardiotoxicity in breast cancer patients undergoing antineoplastic therapies.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Beimmentioning

confidence: 99%

Section: Image Processingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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